Data Science

caret

Question 1
Marks : +2 | -2
Pass Ratio : 100%
For most classification models, each predictor will have a separate variable importance for each class.
True
False
Explanation:
The exceptions are classification trees, bagged trees and boosted trees.
Question 2
Marks : +2 | -2
Pass Ratio : 100%
caret stands for classification and regression training.
True
False
Explanation:
The caret package is a set of functions that attempt to streamline the process for creating predictive models.
Question 3
Marks : +2 | -2
Pass Ratio : 100%
Which of the following function can be used to maximize the minimum dissimilarities?
sumDiss
minDiss
avgDiss
all of the mentioned
Explanation:
sumDiss can be used to maximize the total dissimilarities.
Question 4
Marks : +2 | -2
Pass Ratio : 100%
Which of the following function can be used to flag predictors for removal?
searchCorrelation
findCausation
findCorrelation
none of the mentioned
Explanation:
Some models thrive on correlated predictors.
Question 5
Marks : +2 | -2
Pass Ratio : 100%
The preProcess class can be used for many operations on predictors.
True
False
Explanation:
Operations include centering and scaling.
Question 6
Marks : +2 | -2
Pass Ratio : 100%
Point out the correct statement.
findLinearColumns will also return a vector of column positions can be removed to eliminate the linear dependencies
findLinearCombos will return a list that enumerates dependencies
the function findLinearRows can be used to generate a complete set of row variables from one factor
none of the mentioned
Explanation:
For each linear combination, it will incrementally remove columns from the matrix and test to see if the dependencies have been resolved.
Question 7
Marks : +2 | -2
Pass Ratio : 100%
Which of the following can be used to create sub–samples using a maximum dissimilarity approach?
minDissim
maxDissim
inmaxDissim
all of the mentioned
Explanation:
Splitting is based on the predictors.
Question 8
Marks : +2 | -2
Pass Ratio : 100%
Which of the following curve analysis is conducted on each predictor for classification?
NOC
ROC
COC
All of the mentioned
Explanation:
For two class problems, a series of cutoffs is applied to the predictor data to predict the class.
Question 9
Marks : +2 | -2
Pass Ratio : 100%
Which of the following model sums the importance over each boosting iteration?
Boosted trees
Bagged trees
Partial least squares
None of the mentioned
Explanation:
gbm package can be used here.
Question 10
Marks : +2 | -2
Pass Ratio : 100%
Which of the following can be used to generate balanced cross–validation groupings from a set of data?
createFolds
createSample
createResample
none of the mentioned
Explanation:
createResample can be used to make simple bootstrap samples.